Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container through

Abstract. The paper deals with studying the relationships between the motorcyclists' thinking about proper behaviour and their actual behaviour in the traffic. The impact of some control variables, such as riders' age, experience, driving history, and engine cubature, on actual behaviour, is also addressed here. For the purpose of research, two additional questionnaires were applied besides the well-known Motorcycle Rider Behaviour Questionnaire (MRBQ). To examine the causal relations between all-important latent factors present in this study, the structural equation model was designed. Exploratory and confirmatory factor analyses were also engaged in the analysis and the statistical modelling process. The results show that the higher awareness about alcohol danger and benefits of protective equipment and helmet can noticeably contribute to the bigger traffic safety. Besides, from the results is evident that the control variables are in most cases also significantly interrelated with the actual behaviour factors. The findings of this research could be important for the planning of better traffic safety strategies for the motorcyclists to decrease the fatalities and related costs and traumas.

One of the key issues in modelling for fault detection is how to accommodate the level of detail of the model description to suit the diagnostic requirements. The paper addresses a two-stage modelling concept to an industrial heat exchanger, which is located in a tyre factory. Modelling relies on both, prior knowledge and recorded data. During the identification procedure, the estimates of continuous model parameters are calculated by the least squares method and the state variable filters (SVF). It is shown that the estimates are largely invariant of the bandwidth of the SVFs. This greatly reduces the overall modelling effort and makes the whole concept applicable even for less experienced users. The main issues of the modelling procedure are emphasized. Based on the process model, a simple detection system is derived. An excerpt of the results obtained on operating records is given.

An accurate forecasting system has manifested its role as an enabler in supply chains (SC), which makes the operation possible in a maximally synchronized manner. Its applications have gained the attention of scholars across various disciplines such as forecasting in market behavior analysis and tourism industry; material requirement planning in production; transport and logistics foresight in networks and facilities. Seaports, as specific SC members, are not an exception. Accurate forecasting is needed in almost all aspects of the ports' operation to avoid financial losses related to inappropriate investments and planning. The paper addresses the forecasting of joint demand-supply cargo throughputs in the Adriatic Seaport Koper. The research presents a new forecasting approach, namely, DFA-ARIMAX (Dynamic Factor Analysis-ARIMAX modeling). External economic indicators were screened to obtain useful information using the DFA prior to directing the dynamic factors into the ARIMAX forecasting model. The principal component regression and Monte Carlo framework were included to identify indicators that are unique to the port. Findings revealed that a forecasting system by its enriched capabilities to predict the observed throughputs could be seen as Functional Decision Support System. The benchmarking shows that proposed models outperform competitive models. Practical implications are discussed in detail.